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To date, the efficiency and effectiveness of early warning systems of satellite imagery for preventing and mitigating wildfire remain a challenging issue. The heat of pre-ignition ( $$Q_{{{\text{ig}}}}$$ ) can be an index of fire likelihood, which is further enhanced with remotely sensed data, active fire data, and fuels information for operational application of satellite imagery in fire early warning systems. $$Q_{{{\text{ig}}}}$$ is a prerequisite for forest fires by the side of ignition sources and weather. This study analyzed the effect of $$Q_{{{\text{ig}}}}$$ variation on fire occurrences to develop a remote sensing-based initial fire likelihood index for identifying areas that have a high probability of fire. In this study, $$Q_{{{\text{ig}}}}$$ of Rothermel’s fire spread model daily data is retrieved at 1 km pixels from MODIS data. MODIS active fire products were used to interpret the $$Q_{{{\text{ig}}}}$$ of fuels for 10 days before the days of fire occurrences in November 2010 to determine the pre-fire conditions. A formula for converting $$Q_{{{\text{ig}}}}$$ into an initial fire likelihood index (IFLI) was then used by binary logistic regression method. Analyses show that there was a positive association between suggested IFLI and fire occurrences during the study period with a fair diagnostic accuracy of 92%, and 80% for dead and live fuels, respectively. Mann–kendall test suggested that there are significant trends in the fuel moisture content time-series for both live and dead fuels. Further analysis using the Hosmer–Lemeshow test represents that the models showed an acceptable fit. The suggested IFLI is an effective tool for fire management decision-making whenever a near real-time fire likelihood is required.

Fractal geometry is considered as a new method for quantitative analysis and explanation of surface complexities and roughness in self-similar or self-affine landforms. In the present study, the surface fractal dimensions were investigated by a cellular model by covering divider method and remote sensing data, in a complex morphotectonic region in terms of tectonic, geological, and geomorphological structures along the margin of the High Zagros Belt. Results of this study indicated that surface anomalies can be detected by cellular fractal model due to variations at the boundary of lithological units and structural zones, and along faults that can change the characters of the fractal dimension of landforms. Investigation of wavelet analyses on two profiles of study area shows that the amplitude and frequency of the fractal dimension is related to lithological and structural zones boundaries, and to the presence of faults. In this study, the lowest fractal dimension is associated with the integrated units of Mesozoic orbitolina limestone on the border of the two structural zones of Sanandaj-Sirjan and High Zagros belt. However, the presence of friable and erodible Quaternary formations increases the fractal dimension. There is an inverse relationship between the fractal dimension and elevation and Terrain Ruggedness Index, indicating that mountains have lower fractal dimensions than lowlands. The results of the present study show that fractal dimensional changes in topographically complex zones depend on the interaction of a set of lith-ological, tectonic, and geomorphological factors, and allow for a systematic quantitative analysis of landforms.

Relatively little is known about the causes of landfire assembly in Golestan Province that are subject to environmental and anthropogenic factors. The present study investigated how the landfire hazard is influenced by the environmental and anthropogenic parameters in the fire-prone Hyrcanian forest. The MODIS hotspot data of the past 15 years were collected and analyzed in Golestan Province. The frequencies and distributions of landfires were investigated with 13 environmental and anthropogenic factors selected to construct landfire hazard maps by BLR and ANN methods. The comparison between MODIS active fire detections collected between 2000 and 2015 of the Golestan Province and landfire hazard areas, as predicted by the BLR and ANN, showed satisfactory results for ANN. The results of this study confirmed that anthropogenic variables were important predictors of landfire hazard and showed nonlinear relationships. Vegetation moisture, climate, and topography were also significant variables in the study area.

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash&ndash, Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Remotely-sensed normalized difference vegetation index (NDVI) and land surface temperature (LST) are significant indicators for evaluating the environmental consequences of climate changes in arid regions. Understanding of the relationship between these changes and land surface properties of the arid regions is important in land planning. The Gavkhoni playa is a closed basin in the arid zone of Iran that has recently undergone significant environmental changes due to decreasing precipitations. The objective of this study was to explore the spatiotemporal changes of its LST and NDVI before and after the drought event of 2014–2018. Through the application of an integrated method based on cellular fractal model and continuous wavelet transform (CWT), we investigated the relationship between variation of surface fractal dimension and distribution of LST and NDVI changes before and after the drought. The results indicated substantial changes in LST and NDVI values associated with the onset of drought conditions. Changes in the spatial distribution of LST and NDVI were particularly high in the central playa area, the western and southern sides of the playa, including the salt lake, the wetlands, and the clay flat. These areas correspond to the zone 4 of the fractal dimension map where extreme topographic anomaly and complexity. Wavelet analysis confirmed the relationship between the surface fractal dimension pattern and anomalies in LST and NDVI variations. The investigation highlighted the ability of the integrated application of the cellular fractal model and wavelet analysis to quantify the relationship between land surface properties and the spatial variation of LST and NDVI in changing environmental conditions.

Fires are a major disturbance to forest ecosystems and socioeconomic activities in Mazandaran province, northern Iran, particularly in the Hyrcanian forest sub-region. Mapping the spatial distribution of fire hazard levels and the most important influencing factors is crucial to enhance fire management strategies. In this research, MODIS hotspots were used to represent fire events covering Mazandaran Province over the period 2000-2016. We applied the ecological niche theory through the maximum entropy (MaxEnt) method to estimate fire hazard potential and the association with different anthropogenic and biophysical conditions, by applying different modeling approaches (heuristic, permutation, and jackknife metrics). Our results show that higher fire likelihood is related to density of settlements, distance to roads up to 3 km and to land cover types associated with agricultural activities, indicating a strong influence of human activities in fire occurrence in the region. To decrease fire hazard, prevention activities related to population awareness and the adjustment of farming practices need to be considered.

Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%–12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%–33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations.